Preamble¶
This notebook collects analysis and visualization of all data related to nitrate inventories and other mixed-layer properties as compiled in the previous notebook.
All figures exported from this notebook are prefixed with FIGURE_FN-COMP_.
In [1]:
%load_ext autoreload
%autoreload 2
%run imports.py
In [ ]:
pd.read_feather('../data/no3-compilation/da')
In [27]:
def load_data():
# df_per_station, df = load_data()
df_per_station = pd.read_feather('../data/no3-compilation/database-per-stn.feather')
df = pd.read_feather('../data/no3-compilation/database.feather')
return df_per_station, df
In [3]:
# Define dimensions
dims = dict(
reg_name=hv.Dimension('reg_name', label='Region'),
ntr0=hv.Dimension('ntr0', label='Sfc. NO₃', unit='µM', range=(0,13)),
doy=hv.Dimension('doy', label='Day of the year', range=(0,360)),
lon=hv.Dimension('lon', label='Longitude', unit='°E'),
lat=hv.Dimension('lat', label='Latitude', unit='°N'),
delta_sigth=hv.Dimension('delta_sigth', label='Δσ-θ', unit='kg m⁻³'),
strat=hv.Dimension('strat', label='N²', unit='s⁻²'),
logstrat=hv.Dimension('logstrat', label='log10 N²', unit='s⁻²'),
no3_sfc_winter = hv.Dimension('no3_sfc_winter', label='Pre-bloom sfc. NO₃ ', unit='µM', range=(-.5,13))
)
Maps of key quantities¶
In [4]:
df, dfp, _ = load_data()
In [5]:
def logmean(x):
return np.nanmedian(np.log10(x))
options = (
opts.HexTiles(),
opts.HexTiles('ntr0', cmap=cmocean.cm.matter_r),
opts.HexTiles('delta_sigth', aggregator=logmean),
opts.HexTiles('no3sig_nc', color_levels=8),
opts.HexTiles('no3sig_mld', color_levels=8),
opts.HexTiles('deltanc', symmetric=True, cmap='PuOr'),
opts.Polygons(line_color='k', fill_color=None, line_width=3),
opts.Shape(line_width=1, color='w'),
)
griddict = {
(season, var):
gv.HexTiles(df.loc[df.season==season].dropna(subset=[var]), ['lon', 'lat'], var, label=var)
for season in ['winter', 'summer']
for var in ['nc', 'mld', 'ntr0',
'delta_sigth']
}
gs = hv.GridSpace(griddict, kdims=['season', 'variable'])
gs = gs.redim.range(deltanc=(-50, 50), mld=(0,100), nc=(0.1,100), nc0=(0.1,60))#, delta_sigth=(.05,3))
l = gs * land
html¶
mpl output¶
Make custom diverging color map:
In [6]:
# cm = colorcet.b_diverging_linear_bjy_30_90_c45[::-1]
cm = colorcet.b_diverging_bwr_40_95_c42[::-1]
cm = cm[:127:4] + cm[127:]
In [7]:
obj = l.opts(
opts_map_mpl + [
opts.HexTiles(clim=(0,5), backend='matplotlib'),
]
)
In [8]:
fig = mplrender_map(obj['summer', 'ntr0'], fname='../nb_fig/FIGURE_NO3-COMP_map_no3_summer')
fig
Out[8]:
In [9]:
fig = mplrender_map(obj['winter', 'ntr0'], fname='../nb_fig/FIGURE_NO3-COMP_map_no3_winter')
fig
Out[9]:
Seasonality¶
In [10]:
df, dfp = load_data()
Seasonality by regions¶
In [13]:
loc = gv.Points(df, ['lon', 'lat'], [])
ntr = hv.Scatter(df, 'doy', ['ntr0'])
DataLink(loc,ntr)
l = loc*land*gv.feature.rivers + ntr
l = l.redim(**dims).cols(1)
options = (
opts.Scatter(
tools=['lasso_select', 'box_select'], active_tools=['lasso_select'],
color='lightblue', size=5, selection_color='orange',
height=300, width=600
),
opts.Points(
tools=['lasso_select'], active_tools=['lasso_select'],
projection=ccrs.NorthPolarStereo(),
color='lightblue', size=3, selection_color='orange',
height=400, aspect=1,
),
opts.Feature(
fill_color='wheat', line_color='black',
scale='50m'
),
opts.Feature('Rivers', line_color='blue', scale='110m'),
)
l = l.opts(*options)
hv.save(l, '../nb_fig/FIGURE_NO3-COMP_chart_seasonal_cycle.html')
l
Out[13]:
Make static matplotlib plot for paper¶
In [25]:
def annual_cycle(v):
"""Plots seasonal cycle of variable v"""
sc = hv.Scatter(df, kdims=['doy'],
vdims=[v,
'nc', 'lat', 'lon', 'mld', 'ntr0', 'nc0', 'reg_name'],
label=v
).groupby('reg_name')
err = hvu.bin_average(sc, bins=np.arange(0,361,30))
return sc * err * err.map(hv.Curve, hv.Dataset) * err.map(hv.Scatter, hv.Dataset)
l = annual_cycle('ntr0').layout().cols(3)
l = l.opts(*opts_timeseries_mpl + [
opts.Scatter(title_format='asd{group}',
invert_yaxis=False, backend='matplotlib',
xlabel='', #ylabel='',
),
opts.Layout()
])
l = l.redim(**dims)
# hv.save(l, '../nb_fig/FIGURE_NO3-COMP_chart_seasonal_cycle.png')
with plt.style.context('styles/sparse.mplstyle'):
fig = mplrender(l)
fig.subplots_adjust(wspace=.25, hspace=.08, left=0.2)
fig.set_size_inches((12,8))
fname = '../nb_fig/FIGURE_NO3-COMP_chart_seasonal_cycle'
fig.savefig(fname+'.png')
fig.savefig(fname+'.pdf')
fig
Out[25]:
Laptev Nitrate profiles¶
In [16]:
df_single, df = load_data()
In [17]:
(
df.loc[df.lat.between(76, 77) & df.lon.between(124, 128)]
.hvplot('nitrate', 'depth', by='station')
.opts(invert_yaxis=True)
)
Out[17]:
In [18]:
(
df.loc[df.lat.between(76, 77) & df.lon.between(124, 128)]
.hvplot('sigth', 'depth', by='station')
.opts(invert_yaxis=True)
)
Out[18]:
Gradient:
In [19]:
3/20
Out[19]:
In [20]:
(27-23)/20
Out[20]:
[do later] Interannual trends¶
obs:
- NC deepening in S Beaufort¶
to do: make 2dim hextiles aggregator, then plot hextiles of trends and significance
In [38]:
df_per_station, df = load_data()
df = df.melt(['year', 'season', 'reg_name'], ['ntr0', 'nc', 'nc0']).dropna(subset=['season'])
df = df.loc[~df.reg_name.isnull()]
In [39]:
df.reg_name.unique()
Out[39]:
In [40]:
m = df.hvplot.scatter('year', 'value', groupby=['reg_name', 'season', 'variable'])
from utils.holoviews.element import Regression
l=m*m.map(Regression, 'Scatter')
In [49]:
%%opts Scatter [ frame_width=600 frame_height=400 show_grid=True invert_yaxis=True, legend_position='top']
l.overlay('season')#.layout('reg_name').cols(1)
Out[49]:
[unused] Scatter plots: Relations¶
deltanc vs surface NO3
NC vs MLD...